Autonomous agents, including service robots, require adherence to moral values, legal regulations, and social norms to interact effectively with humans. A vital aspect of this is the acquisition of ownership relationships between humans and their carrying items, which leads to practical benefits and a deeper understanding of human social norms. The proposed framework enables the robots to learn item ownership relationships autonomously or through user interaction. The autonomous learning component is based on Human-Object Interaction (HOI) detection, through which the robot acquires knowledge of item ownership by recognizing correlations between human-object interactions. The interactive learning component allows for natural interaction between users and the robot, enabling users to demonstrate item ownership by presenting items to the robot. The learning process has been divided into four stages to address the challenges posed by changing item ownership in real-world scenarios. While many aspects of ownership relationship learning remain unexplored, this research aims to explore and design general approaches to item ownership learning in service robots concerning their applicability and robustness. In future work, we will evaluate the performance of the proposed framework through a case study.